Existing process control systems may perform periodic diagnostics on process control devices, such as valves, to determine the operability and performance of such devices. Determining the operability of a process control device may permit better scheduling of maintenance of the process control device, thereby decreasing failure occurrences and down time. This may result in increased efficiency, safety, and revenue. The process control systems may use various sensors and other measurement devices to observe characteristics of a process control device. For example, some existing control systems may use a digital valve controller to measure and collect data from various sensors on a control valve.
Among the uses of data collected from control valves, customers desire the data to plan preventative maintenance for their process plants, hoping to avoid unplanned maintenance and loss of production cause by unexpected failures. Customers, for example, will want to know the projected life span of a valve, before requiring maintenance, as well as what repair procedures and replacement options are available and recommended. For the manufacturer, providing a precise life span prediction is challenging because actual process conditions will vary dramatically from customer to customer, or facility to facility, even within a processing plant. Specification sheets may be provided to the customers providing some projection data, and sometimes in response to customer provided design conditions. However, factors such as temperature and pressure often vary dramatically from those provided in the design conditions from the customer and either way, other varying conditions such as fluid state (liquid or vapor) and impurities (solid, liquid, or vapor) are typically not provided in the design conditions, or, as with the other factors, can vary considerably during actual use.
Conventionally, service and repair history data from customers would be collected to create Mean Time To Failure (MTTF) and Mean Time Between Failure (MTBF). This MTTF and MTBF data could then be used for predicting life span of a valve. Using this historical data can be limiting, however, because maintenance records may be incomplete or non-existent. Furthermore, customers may not desire to share such information out of a concern that their operating conditions would be disclosed to their competitors. The result is that MTTF and MTBF data, based on historical data, are often incomplete and not sufficiently informative.
Another technique for predicting MTTF and MTBF is through the use of laboratory data produced in conditions as closely approximating real life conditions as possible. Pressure and temperature conditions are usually easy to achieve in a well-equipped lab. Fluid properties and contaminations, however, are much more difficult to simulate; although the essential fluid properties typically can be achieved, i.e., oxidizing, non-oxidizing, wet, dry, lubricating and non-lubricating. Occasionally, even a known contamination can be achieved such as with particulates in the fluid stream. Laboratory cycle testing in particular, e.g., at the same temperature, pressure and fluid properties that represent particular valve service applications, can be an effective ersatz for actual field data. This is especially the case for valve components that are subject to normal mechanical wear or fatigue.
While laboratory testing is used, for the foregoing and other reasons, conventional testing methods of determining MTTF and MTBF are lacking. The methods are unable to account for the varied conditions and various factors that affect device life span, particularly, those relating to sliding stem valves and rotary valves, where the various components that can wear or fatigue, resulting in valve failure, are many and each with potentially different responses to operating conditions, such as temperature, pressure, fluid, etc.